Current digitalized manufacturing systems do not yet achieve the goal of smart manufacturing: precise con-trol and agility under unexpected disruptions. Push-Pull Digital Thread is a solution concept to enable contex-tual data and knowledge exchange across operational and functional units in a manufacturing enterprise. The extraction of decision reasoning and functional information can be facilitated by Large Language Models proc-essing information obtained from a decision maker at the point of decision. This concept shows a potential to address critical limitations in previous endeavours for smart manufacturing systems by building a semanti-cally searchable and sharable knowledge base in manufacturing systems and beyond.

The present article proposes a novel smart building energy system utilizing deep geothermal resources through naturally-driven borehole thermal energy storage interacting with the district heating network. It includes an intelligent control strategy for lowering operational costs, making better use of renewables, and avoiding CO2 emissions by eliminating heat pumps and cooling machines to address the heating and cooling demands of a commercial building in Uppsala, a city near Stockholm, Sweden. After comprehensively conducting techno-environmental and economic assessments, the system is fine-tuned using artificial neural networks (ANN) for optimization. The study aims to determine which ANN design and training procedure is the most efficient in terms of accuracy and computing speed. It also assesses well-known optimization algorithms using the TOPSIS decision-making technique to find the best trade-off among various indicators. According to the parametric results, deeper boreholes can collect more geothermal energy and reduce CO2 emissions. However, deep drilling becomes more expensive overall, suggesting the need for multi-objective optimization to balance costs and techno-environmental benefits. The results indicate that Levenberg-Marquardt algorithms offer the optimum trade-off between computation time and error minimization. From a TOPSIS perspective, while the dragonfly algorithm is not ideal for optimizing the suggested system, the non-dominated sorting genetic algorithm is the most efficient since it yields more ideal points rated below 100. The optimization yields a higher energy production of 120 kWh/m2, as well as a decreased levelized cost of energy of 57 $/MWh, a shorter payback period of two years, and a reduced CO2 index of 1.90 kg/MWh. The analysis reveals that despite the high investment costs of 382.50 USD/m2, the system is financially beneficial in the long run due to a short payback period of around eight years, which aligns with the goals of future smart energy systems: reduce pollution and increase cost-effectiveness.

Background: This study performs an exploratory analysis of current-future sustainability challenges for ocean planning for the regional seas of Catalonia located in the Western Mediterranean (Spain). Methods: To address the challenges we develop an Maritime Spatial Planning (MSP)-oriented geodatabase of maritime activities and deploy three spatial models: 1) an analysis of regional contribution to the 30% protection commitment with Biodiversity Strategy 2030; 2) a spatial Maritime Use Conflict (MUC) analysis to address current and future maritime activities interactions and 3) the StressorGenerator QGIS application to locate current and anticipate future sea areas of highest anthropogenic stress. Results & Conclusions: Results show that the i) study area is one of the most protected sea areas in the Mediterranean (44–51% of sea space protected); ii) anthropogenic stressors are highest in 1–4 nautical miles coastal areas, where maritime activities agglomerate, in the Gulf of Roses and Gulf of Saint Jordi. iii) According to the available datasets commercial fishery is causing highest conflict score inside protected areas. Potential new aquaculture sites are causing highest conflict in Internal Waters and the high potential areas for energy cause comparably low to negligible spatial conflicts with other uses. We discuss the added value of performing regional MSP exercises and define five challenges for regional ocean sustainability, namely: Marine protection beyond percentage, offshore wind energy: a new space demand, crowded coastal areas, multi-level governance of the regional sea and MSP knowledge gaps.

Diamond possesses extraordinary properties, including extreme hardness, thermal conductivity, and mechanical strength. Global industrial diamond production is dominated by synthetic diamond, with important commercial applications in hard coatings and semiconductors. However, the life cycle impacts of synthetic diamond materials are largely unknown. The main aim of this study is to conduct the first detailed life cycle assessments of the typical production routes for nanodiamond and diamond film, which are detonation synthesis and microwave chemical vapor deposition, respectively. The functional units were set to 1 g nanodiamond and 1 cm2 diamond film. A limited number of inputs dominate the assessed impacts: explosives and cooling water for nanodiamond production, and electricity and substrate for diamond film production. Diamond film manufacturers can reduce their global warming, freshwater eutrophication, and terrestrial acidification impacts by 62-71% by sourcing wind or solar instead of global average electricity. However, this comes at the expense of increased mineral resource scarcity impacts at 57-73%. A comparison between nanodiamond and synthetic diamond grit shows that the grit's global warming impact is about 5 times higher, suggesting that nanodiamond is environmentally preferable. The ready-to-use unit-process data from this study can be applied in future studies of products containing these materials.

Reduced climate impacts of the information and communication technology (ICT) sector is required to redirect digital technologies towards sustainability. Life cycle assessment (LCA) can be used to quantify climate impacts of the ICT sector and identify hotspots. Several studies have assessed the direct climate impact of the global ICT sector and arrived at quite different conclusions regarding its future impacts. The aims of this paper are to identify key aspects for future scenarios in LCAs of the ICT sector's direct climate impact and to highlight related challenges. For this, a literature screening on direct climate impacts of the ICT sector was conducted. Preliminary findings indicate that for end-user devices, the number of units is a key aspect in influencing the climate impact of the future ICT sector. For telecommunication networks and data centers, the growth in data traffic and energy efficiency improvements are key aspects. In addition, the carbon intensity of electricity generation and the lifetime of products are key aspects for all ICT subdomains (i.e., end-user devices, telecommunication networks and data centers). These key aspects significantly influence the ICT sector's climate impact and need to be carefully considered in future studies. The authors future research includes to finalize the in-depth review and to develop a framework for LCAs of the ICT sector's direct impacts.

The ‘decade of action’ intended to accomplish the ambitious 17 Sustainable Development Goals (SDGs) faces notable challenges. Our investigation into the impact of the COVID-19 crisis on SDG progress reveals important lessons for shaping effective policy interventions to ensure resilient societies and overall well-being. Through systematic mapping and a rapid review approach, our analysis reveals that nearly 90% of the SDGs, specifically 144 targets, were adversely affected by the COVID-19 pandemic. Yet, there is a glimmer of opportunity: 66 targets stand to gain from the crisis-induced transformations, provided that the right choices are made. Achieving this goal demands a comprehensive approach and decisive leadership to steer an inclusive economic recovery that also safeguards the environment while safeguarding the environment. The intricate interplay between the ongoing planetary and post-COVID-19 crises, environmental challenges, and conflicts underscores the need for a proactive, deliberate and well-informed approach, marked by collaborative decision-making, which is imperative for effectively steering the ‘decade of action’ toward achieving the SDGs. These complex challenges demand collective, decisive action, all with the overarching aim of securing a just and sustainable future for all.

This study explores the topics and trends of teaching AI ethics in higher education, using Latent Dirichlet Allocation as the analysis tool. The analyses included 166 courses from 105 universities around the world. Building on the uncovered patterns, we distil a model of current pedagogical practice, the BAG model (Build, Assess, and Govern), that combines cognitive levels, course content, and disciplines. The study critically assesses the implications of this teaching paradigm and challenges practitioners to reflect on their practices and move beyond stereotypes and biases.

Universal clean cooking is a key target under Sustainable Development Goal (SDG) 7, with implications for several other SDGs, such as good health, gender equality and climate. Yet, 2.4 billion people globally still lack access to clean cooking. The situation is especially dire in sub-Saharan Africa (SSA), where only 17% use clean options. We develop OnStove, an open-source spatial tool comparing the relative potential of different cookstoves on the basis of their costs and benefits, and apply it to SSA. Our results suggest a severe market failure as the currently most used solution, traditional biomass, produces the lowest social net-benefits nearly everywhere in SSA. Correcting this failure, which stems from multiple market and behavioural obstacles, would deliver significant health, time and emission benefits but requires identification and promotion of policies to transform cooking energy use. Spatial mapping offers a more nuanced understanding of the costs needed to deliver cleaner cooking transitions than was previously possible, which is useful for improved targeting of intervention strategies.

Access to clean cooking and electricity are both targets of Sustainable Development Goal 7 (SDG 7). While both targets are to be reached by 2030, the progress towards them is uneven. Peer-reviewed literature and policy documents have called for more integrated planning efforts accounting for both targets simultaneously. Here, we soft-link for the first time a geospatial electrification tool (OnSSET) with a geospatial clean cooking tool (OnStove) to allow for integrated planning in a case-study of Kenya. In 2021, 77% of Kenyans had access to electricity, but only 28% to clean cooking. The government has targeted universal electricity and clean cooking access by 2026 and 2028 respectively, and the country has a large potential for electric cooking. Our results show how incorporating cooking demand in the electricity model, favors centralized options as these benefit from economies of scale. Without soft-linking, 77% of the population benefit most from adopting an electric option. With an integrated approach, these shares increase to between 85 and 91%. We find that an integrated approach is important for understanding the best way forward towards the achievement of SDG 7.

Loss and Damage benefits from the inclusion of Artificial Intelligence systems to support prevention and assessment. As AI research and development is highly dominated by western and private-led powers, the effectiveness of its use is limited for vulnerable countries. We call for an accessible, inclusive and locally-grounded AI to serve the needs of the most vulnerable, support Article 8 of the Paris Agreement and democratise innovation.

Background: A large proportion of individuals experience functional impairment in everyday life due to climate worry. However, the current understanding of this functional impairment is limited by the use of suboptimal measures. Furthermore, it is not known whether functional impairment due to climate worry affects pro-environmental behaviors (PEBs) or whether individuals who experience such impairment perceive a need for support. The aims of the current study were (1) to extend previous research using an established measure of functional impairment (the Work and Social Adjustment Scale, WSAS), (2) to explore the associations between climate worry, functional impairment, and PEBs, and (3) to describe the characteristics and the perceived need for support of individuals with functional impairment due to climate worry.

Methods: A cross-sectional survey targeting adult individuals who experience climate worry. Participants were recruited nationally in Sweden between September and October 2022. The survey included measures of climate worry severity, climate worry frequency, functional impairment, PEBs, depressive symptoms, sleep problems, and questions related to perceived need for support.

Results: A total of 1221 adults (75% women, mean age 46.3 years) were included in the analyses. Multivariate structural equation modeling revealed that climate worry severity and frequency were significantly associated with PEBs (β = 0.34 and β = 0.45, respectively). Climate worry frequency was associated with functional impairment (β = 0.41). Functional impairment was only marginally associated with PEBs (β = 0.05). Approximately 40% of the sample (n = 484) reported a high frequency and high severity of climate worry. Among these, one-third (n = 153) scored above the cutoff for significant impairment on the WSAS. Individuals in this group (high severity and frequency of climate worry as well as significant functional impairment) were more likely to experience depressed mood and sleep problems and were more interested in receiving support, specifically concerning strategies for worry management and sustainable behavior change.

Conclusions: Using an established measure of functional impairment, we found an association of climate worry with functional impairment and PEBs. Importantly, as there is a perceived need for support in individuals with impairment due to climate worry, interventions targeting this specific subgroup should be developed.

Information communication and technology (ICT) services and solutions can improve resource efficiency in a variety of sector, but also result in direct environmental impacts. This study assesses the direct environmental impacts of an ICT service that improves vehicle fuel efficiency using a cradle-to-grave life cycle assessment (LCA). This is one of the first studies to examine the entire life cycle of an ICT service from development to use and maintenance, with a focus on software—an aspect that is typically neglected in previous studies. The results suggest that software development and maintenance and the use of in-vehicle communicators for data transmission have the largest environmental impacts across multiple categories. Deployed across a fleet of 150,000 vehicles over 5 years, we estimate that the ICT service is responsible for 174 tCO2e. However, this is negligible compared with the total emissions of the fleet and the potential savings from the service, given a single diesel vehicle in this fleet emits around 130 tCO2e over the same period. We explore several scenarios to reduce the footprint of the ICT service. The largest potential reduction of around one-third is achieved by replacing in-house servers with cloud computing in a data center located in a region with low-carbon electricity. The study demonstrates how LCA can be used to assess the environmental impacts of ICT services and the importance of considering software in these assessments.

Climate models are developed based on well-established physical principles applied to past and recent climate changes. There is considerable confidence that the models can also provide estimates of some climate variables (i.e., surface temperature, CO2 levels, ocean heat content). Despite advanced mathematical developments in the field of climate modeling, the existing climate models suffer from the following major limitations: first, the models do not consider that their estimations will be highly unreliable when a tipping point is triggered; secondly, many of the environmental tipping points are already triggered, however their existence is overlooked; and third, the existing climate models do not consider the interrelations among the tipping points (i.e., one tipping point can trigger other tipping points to be tipped more rapidly). Our objective is to describe the importance of environmental "tipping points," the importance of which is often ignored or downplayed in relevant literature. Our analysis, based on extensive multidisciplinary literature searches, reveals that there are many environmental tipping points which are overlooked in climate-modeling studies. We argue that climate modeling could be improved when the tipping points and their interrelations are all considered within the modeling process. We further discuss two other important issues regarding environmental tipping points: first, all tipping points might not be as impactful on the climate system, therefore their relative impacts should be ranked; second, it is in principle impossible to know the exact number of environmental tipping points, therefore even though it could be possible to devise improvements to the existing climate models with our suggestions, it may be impossible to achieve a perfect model to estimate the climate variables of the upcoming years. The remainder of this paper is structured as follows: In the background section, we introduce research on tipping points within commonly used climate models. We explain the aerosol masking effect and ocean dynamics with respect to their commonly overlooked roles as important contributors to environmental change. We introduce remote sensing and AI methods that serve as promising approaches for identification of currently unknown tipping points. We mention perturbation theory, a standard set of mathematical methods in physics that serves as a potentially systematic method to rank environmental tipping points according to their impact on extant climate models. In the discussion section, we make suggestions regarding further research on identifying the typically overlooked tipping points, and we make suggestions to improve climate models by considering additional information presented in the current paper. Finally, we conclude this article summarizing our chief methodological recommendations.

The world should redouble its efforts on the SDGs, not abandon them. Here's how to progress the United Nations' agenda towards 2050.

Decarbonisation plans across the globe require zero-carbon energy sources to be widely deployed by 2050 or 2060. Solar energy is the most widely available energy resource on Earth, and its economic attractiveness is improving fast in a cycle of increasing investments. Here we use data-driven conditional technology and economic forecasting modelling to establish which zero carbon power sources could become dominant worldwide. We find that, due to technological trajectories set in motion by past policy, a global irreversible solar tipping point may have passed where solar energy gradually comes to dominate global electricity markets, without any further climate policies. Uncertainties arise, however, over grid stability in a renewables-dominated power system, the availability of sufficient finance in underdeveloped economies, the capacity of supply chains and political resistance from regions that lose employment. Policies resolving these barriers may be more effective than price instruments to accelerate the transition to clean energy.

The digital revolution has brought ethical crossroads of technology and behavior, especially in the realm of sustainable cities. The need for a comprehensive and constructive ethical framework is emerging as digital platforms encounter trouble to articulate the transformations required to accomplish the sustainable development goal (SDG) 11 (on sustainable cities), and the remainder of the related SDGs. The unequal structure of the global system leads to dynamic and systemic problems, which have a more significant impact on those that are most vulnerable. Ethical frameworks based only on the individual level are no longer sufficient as they lack the necessary articulation to provide solutions to the new systemic challenges. A new ethical vision of digitalization must comprise the understanding of the scales and complex interconnections among SDGs and the ongoing socioeconomic and industrial revolutions. Many of the current social systems are internally fragile and very sensitive to external factors and threats, which lead to unethical situations. Furthermore, the multilayered net-like social tissue generates clusters of influence and leadership that prevent communities from a proper development. Digital technology has also had an impact at the individual level, posing several risks including a more homogeneous and predictable humankind. To preserve the core of humanity, we propose an ethical framework to empower individuals centered on the cities and interconnected with the socioeconomic ecosystem and the environment through the complex relationships of the SDGs. Only by combining human-centered and collectiveness-oriented digital development will it be possible to construct new social models and interactions that are ethical. Thus, it is necessary to combine ethical principles with the digital innovation undergoing in all the dimensions of sustainability.

This study aimed at supporting robust decision-making for planning and management of water-energy-food Nexus systems in the country of Jordan. Nexus priority challenges in Jordan were identified as (1) water scarcity, (2) agricultural productivity and water quality, and (3) shift to energy independence. We created a water-energy-food Nexus model that integrates three modelling frameworks: (1) the Water Evaluation and Planning system WEAP model to estimate water demands, supplies and allocation; (2) the MABIA model to estimate crop production, and, (3) a GIS-based energy modelling tool to estimate energy requirements of the water system. Through a set of scenario runs, results show how desalination is needed to address water scarcity, but it has to be coupled with low-carbon electricity generation in order to not exacerbate climate change. Improving water productivity in agriculture improves most of the studied dimensions across the water-energy-food security nexus; however, it does little for water scarcity at the municipal level. Reducing non-revenue water can have positive effects on municipal unmet demand and reduction of energy for pumping, but it does not improve agricultural water productivity and may have negative feedback effects on the Jordan Valleys aquifer levels. Energy efficiency can support energy-intensive projects, like desalination, by substantially reducing the load on the energy system, preventing increased emissions and achieving a more resilient water system. Finally, when all interventions are considered together all of the major drawbacks are reduced and the benefits augmented, producing a more holistic solution to the WEF Nexus challenges in Jordan.

Background: Cooking with traditional fuels can lead to severe health issues caused by household air pollution, and can also affect gender equality and drive environmental degradation. In Nepal, despite government efforts to promote electric cooking, more than half of the population still uses traditional fuels, with electric cooking adoption remaining below 1%. Several of the barriers to and enablers of clean cooking vary geographically; however, few studies have considered spatial explicit information in planning national-scale transitions to clean cooking. In this study we provide a spatially explicit roadmap to estimate the required investments and benefits gained from the transition across Nepal. Methods: This study uses geospatial modelling methods to evaluate strategies to achieve the Government of Nepal's vision for a national-scale transition to clean cooking. We integrate the open-source clean cooking geospatial assessment tool OnStove and a spatial multicriteria analysis model. With OnStove, we evaluate which cooking technologies and fuels maximise the net benefits of a clean-cooking transition across each km2 of the region. With the multicriteria analysis, we weigh stakeholder preferences and prioritise areas of action where policy should be implemented. We used the most up-to-date geospatial data to the year 2023, such as the High Resolution Settlement Layer, Open Street Maps’ road networks, the Global Human Settlement Layer, NASA/USGS forest cover maps, and Facebook's Relative Wealth Index, among others. We also relied on data from the Nepal Oil Corporation, the Nepal Electricity Agency, the Central Bureau of Statistic's 2021 national census, and the Alternative Energy Promotion Center. We evaluate four scenarios capturing advances on clean cooking policy up to the year 2022, current market inefficiencies, and the potential effects of new policies for clean-cooking transition in Nepal. Findings: Our results show that transitional and clean cooking technologies provide higher net benefits than traditional options everywhere across Nepal in all scenarios. Our net-benefit analysis shows that around 9563 deaths could be averted yearly if benefits and externalities were perceived and valued correctly. Furthermore, substantial benefits could be achieved in regard to greenhouse gas emissions avoidance, time saved, and health-cost reductions. Our results also show that the current subsidy strategy from the Government of Nepal is well aligned with the benefits achieved under a cost–benefit analysis. In this context, electric cooking can bring the highest benefits to the largest part of the population. The analysis showed how high subsidies for liquefied petroleum gas in Nepal can present trade-offs with energy security and independence, and how this could be avoided by transferring part of the subsidy to cover differentiated electric cooking tariffs. Accounting for stakeholder preferences and sociodemographic and geographical differences to prioritise areas of focus can balance affordability constraints and target the most vulnerable people first, thus achieving integrated and inclusive planning. Interpretation: Using spatially explicit modelling approaches to evaluate strategies for a clean cooking transition can provide more nuanced results that have not been possible before. This approach can enable data-driven and integrated planning to help to understand which locations of a study area should be prioritised for policy application. Integrated planning can help to reduce affordability constraints on the population and design strategies for a sustainable and inclusive transition. These strategies allow financial institutions, donors, impact investors, development organisations, and government agencies to use their resources, funds, and assistance to create a large impact. Funding: Clean Cooking Alliance.

Understanding the progress of the Sustainable Development Goals (SDGs) proposed by the United Nations (UN) is important, but difficult. In particular, policymakers would need to understand the sentiment within the public regarding challenges associated with climate change. With this in mind and the rise of social media, this work focuses on the task of uncovering the sentiment of Twitter users concerning climate-related issues. This is done by applying modern natural-language-processing (NLP) methods, i.e. VADER, TextBlob, and BERT, to estimate the sentiment of a gathered dataset based on climate-change keywords. A transfer-learning-based model applied to a pre-trained BERT model for embedding and tokenizing with logistic regression for sentiment classification outperformed the rule-based methods VADER and TextBlob; based on our analysis, the proposed approach led to the highest accuracy: 69%. The collected data contained significant noise, especially from the keyword 'energy'. Consequently, using more specific keywords would improve the results. The use of other methods, like BERTweet, would also increase the accuracy of the model. The overall sentiment in the analyzed data was positive. The distribution of the positive, neutral, and negative sentiments was very similar in the different SDGs.

Access to electricity is a prerequisite for development, included in both the Agenda for Sustainable Development and the African Union’s Agenda 2063. Still, universal access to electricity is elusive to large parts of the global population. In Somalia, approximately one-third of the population has access to electricity. The country is unique among non-island countries as it has no centralized grid network. This paper applies a geospatial electrification model to examine paths towards universal access to electricity in Somalia under different timelines and with regard to different levels of myopia in the modeling process. This extends the previous scientific literature on geospatial electrification modeling by studying the effect of myopia for the first time and simultaneously presenting the first geospatial electrification analysis focused on Somalia. Using the Open Source Spatial Electrification Tool (OnSSET), the least-cost electrification options towards 2030 and 2040, respectively, are compared. We find that under the shorter timeline, a deployment of mini-grids and stand-alone PV technologies alone provides the least-cost option under all but one scenario. However, under the longer timeline, the construction of a national transmission backbone would lower overall costs if there is high demand growth and/or low cost of centralized grid electricity generation. We also compare different levels of myopia in the modeling process. Here, OnSSET is first run directly until 2040, then in five-year time-steps and annual time-steps. We find that running the model directly until 2040 leads to the lowest costs overall. Running the model myopically leads to a sub-optimal, more costly technology mix, with a lock-in effect towards stand-alone systems. On the other hand, the myopic approach does provide additional insights into the development of the system over time. We find that longer-term planning favors the centralized grid network, whereas short-sighted myopic planning can lead to higher costs in the long term and a technology mix with a higher share of stand-alone PV.

As of 2021, 675 million people globally lack access to electricity. Geospatial electrification tools can be used to identify the mix of grid-extension, mini-grids and stand-alone technologies that can supply currently unelectrified areas at the lowest cost. Several such tools have been developed, at different levels of modelling detail and complexity. In this paper, we improve the Open Source Spatial Electrification Tool (OnSSET) to develop a flexible geospatial electrification tool that can still run lighter rapid assessments for a first estimate of the technology split, but now also more detailed analysis with higher spatial and temporal resolution used for grid routing, distribution network design and optimization of hybrid mini-grid generation introduced through new algorithms. We compare the existing light and new detailed versions of the tool through a case study of the north-western parts of the Democratic Republic of the Congo. We find that the new grid routing algorithm lead to more off-grid technologies, and that the detailed design of distribution networks lead to a reduction in stand-alone technologies. The detailed optimization of hybrid mini-grids display varying effects at different demand levels. Given the increased computational effort that is observed with higher modelling detail, we discuss the implications for scenario design and selection of geospatial electrification tool for future analyses aiming to support the achievement of SDG 7.

The 2030 Agenda of the United Nations (UN) revolves around the Sustainable Development Goals (SDGs). A critical step towards that objective is identifying whether scientific production aligns with the SDGs' achievement. To assess this, funders and research managers need to manually estimate the impact of their funding agenda on the SDGs, focusing on accuracy, scalability, and objectiveness. With this objective in mind, in this work, we develop ASDG, an easy-to-use Artificial-Intelligence-based model for automatically identifying the potential impact of scientific papers on the UN SDGs. As a demonstrator of ASDG, we analyze the alignment of recent aerospace publications with the SDGs. The Aerospace data set analyzed in this paper consists of approximately 820,000 papers published in English from 2011 to 2020 and indexed in the Scopus database. The most-contributed SDGs are 7 (on clean energy), 9 (on industry), 11 (on sustainable cities), and 13 (on climate action). The establishment of the SDGs by the UN in the middle of the 2010 decade did not significantly affect the data. However, we find clear discrepancies among countries, likely indicative of different priorities. Also, different trends can be seen in the most and least cited papers, with apparent differences in some SDGs. Finally, the number of abstracts the code cannot identify decreases with time, possibly showing the scientific community's awareness of SDG.

In this chapter we extend earlier work (Vinuesa et al., Nat Commun 11, 2020) on the potential of artificial intelligence (AI) to achieve the 17 Sustainable Development Goals (SDGs) proposed by the United Nations (UN) for the 2030 Agenda. The present contribution focuses on three SDGs related to healthy and sustainable societies, i.e., SDG 3 (on good health), SDG 11 (on sustainable cities), and SDG 13 (on climate action). This chapter extends the previous study within those three goals and goes beyond the 2030 targets. These SDGs are selected because they are closely related to the coronavirus disease 19 (COVID-19) pandemic and also to crises like climate change, which constitute important challenges to our society.

Artificial intelligence (AI) impacts our journey in reaching the United Nation’s Sustainable Development Goals in more ways than one. In the WASP-HS report “AI, sustainability and Agenda 2030”, the conclusions from the community reference meeting that took place on October 4, 2023, are presented. The contribution formulates and discusses the following challenges: First, that the desire to make environmental data politically actionable in immediate ways is understandable but requires carefully qualifying the aims. Data circulation accelerates various forms of data use, including commercial and for-profit use. Second, that data accommodates different stakeholder views and opposing interests. The same data sets can support diverging understandings of “sustainability”.